Skip to main content
SpringerLink
Log in

Rapid Non-destructive Detection of Spoilage of Intact Chicken Breast Muscle Using Near-infrared and Fourier Transform Mid-infrared Spectroscopy and Multivariate Statistics

  • Original Paper
  • Published:
Food and Bioprocess Technology Aims and scope Submit manuscript

Abstract

Near-infrared (NIR) transflectance and Fourier transform-infrared (FT-IR) attenuated total reflectance spectra of intact chicken breast muscle packed under aerobic conditions and stored at 4° for 14 days were collected and investigated for their potential use in rapid non-destructive detection of spoilage. Multiplicative scatter correction-transformed NIR and standard normal variate-transformed FT-IR spectra were analysed using principal component analysis (PCA), partial least-squares discriminant analysis (PLS2-DA) and outer product analysis (OPA). PCA and PLS2-DA regression failed to completely discriminate between days 0 and 4 samples (total viable count (TVC) days 0 and 4 = 5.23 and 6.75 log10 cfu g−1) which had bacterial loads smaller than the accepted levels (8 log10 cfu g−1) of sensory spoilage detection but classified correctly days 8 and 14 samples (TVC days 8 and 14 = 9.61 and 10.37 log10 cfu g−1). OPA performed on both NIR and FT-IR datasets revealed several correlations that highlight the effect of proteolysis in influencing the spectra. These correlations indicate that increase in free amino acids and peptides could be the main factor in the discrimination of intact chicken breast muscle. This investigation suggests that NIR and FT-IR spectroscopy can become useful, rapid, non-destructive tools for spoilage detection.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Adams, M. R., & Moss, M. O. (2000). Food microbiology (2nd ed.). UK: The Royal Society of Chemistry, MPG Books.

    Google Scholar 

  • Aho, P. (2006). Broiler economics bulletin, 14(6), Arbor Acres, USA.

  • Alexandrakis, D., Downey, G., & Scannell, A. G. M. (2008). Detection and identification of bacteria in an isolated system with near-infrared spectroscopy and multivariate analysis. Journal of Agricultural and Food Chemistry, 56(10), 3431–3437.

    Article  CAS  Google Scholar 

  • Ammor, M. S., Argyri, A., & Nychas, G. J. E. (2009). Rapid monitoring of the spoilage of minced beef stored under conventionally and active packaging conditions using Fourier transform infrared spectroscopy in tandem with chemometrics. Meat Science, 81(3), 507–514.

    Article  CAS  Google Scholar 

  • Barros, A. S., Mafra, I., Ferreira, D., Cardoso, S., Reis, A., Lopes da Silva, J. A., et al. (2002). Determination of the degree of methylesterification of pectic polysaccharides by FT-IR using an outer product PLS1 regression. Carbohydrate Polymers, 50, 85.

    Article  CAS  Google Scholar 

  • Barros, A. S., Safar, M., Devaux, M. F., Robert, P., Bertrand, D., & Rutledge, D. N. (1997). Relations between mid-infrared and near infrared spectra detected by analysis of variance of an intervariable data matrix. Applied Spectroscopy, 51(9), 1384.

    Article  CAS  Google Scholar 

  • Brown, H. M. (1982). Meat microbiology (pp. 86–89). Essex: Applied Sciences Publishers Ltd.

    Google Scholar 

  • Byler, D. M., & Susi, H. (1986). Examination of the secondary structure of proteins by deconvolved FTIR spectra. Biopolymers, 25, 469–487.

    Article  CAS  Google Scholar 

  • Champiat, D., Matas, N., Monfort, B., & Fraass, H. (2001). Application of biochemiluminescence to HACCP. Luminescence, 16, 193–198.

    Article  CAS  Google Scholar 

  • Dainty, R. H. (1996). Chemical/biochemical detection of spoilage. International Journal of Food Microbiology, 33, 19–33.

    Article  CAS  Google Scholar 

  • Di Natale, C., Zude-Sasse, M., Macagnano, A., Paolesse, R., Herold, B., & D’Amico, A. (2002). Outer product analysis of electronic nose and visible spectra: application to the measurement of peach fruit characteristics. Analytica Chimica Acta, 459, 107.

    Article  Google Scholar 

  • Dubois, J., Lewis, E. N., Fry, F. S., Jr., & Calvey, E. M. (2005). Bacterial identification by near-infrared imaging of food specific-cards. Food Microbiology, 22, 577–583.

    Article  CAS  Google Scholar 

  • Ellis, D. I., Broadhurst, D., Bell, D. B., Rowland, J. J., & Goodacre, R. (2002). Rapid and quantitative detection of the microbial spoilage of meat by Fourier transform infrared spectroscopy and machine learning. Applied and Environmental Microbiology, 68(6), 2822–2828.

    Article  CAS  Google Scholar 

  • Gill, C. O., & Newton, K. G. (1978). The ecology of bacterial spoilage of fresh meat at chill temperatures. Meat Science, 2, 207–217.

    Article  CAS  Google Scholar 

  • Hennessy, S., Downey, G., & O’Donnell, C. (2008). Multivariate analysis of attenuated total reflection–Fourier transform infrared spectroscopic data to confirm the origin of honeys. Applied Spectroscopy, 62(10), 1115–1123.

    Article  CAS  Google Scholar 

  • Hong, Y., Berrang, M. E., Liu, T., Hofacre, C. L., Sanches, S., Wang, L., et al. (2003). Rapid detection of Campylobacter coli, C. jejuni, and Salmonella enterica on poultry carcasses by using PCR enzyme linked immunosorbent assay. Applied and Environmental Microbiology, 69, 3492–3499.

    Article  CAS  Google Scholar 

  • Jaillais, B., Pinto, R., Barros, A. S., & Rutledge, D. N. (2004). Outer-product analysis (OPA) using PCA to study the influence of temperature on NIR spectra of water. Vibrational Spectroscopy, 39(1), 50–58.

    Article  Google Scholar 

  • Lin, M., Al-Holy, M., Mousavi-Hesary, M., Al-Qadiri, H., Cavinato, A. G., & Rasco, B. A. (2004). Rapid and quantitative detection of the microbial spoilage in chicken meat by diffuse reflectance spectroscopy (600–1100 nm). Letters in Applied Microbiology, 39, 148–155.

    Article  CAS  Google Scholar 

  • Liu, F., He, Y., Wang, L., & Sun, G. (2009). Detection of organic acids and pH of fruit vinegars using near-infrared spectroscopy and multivariate calibration. Food and Bioprocess Technology. doi:10.1007/s11947-009-0240-9.

  • Mossoba, M. M. (1999). Spectral methods in food analysis. New York: Marcel Dekker.

    Google Scholar 

  • Nychas, G. J. E., Dillon, V. M., & Board, R. G. (1988). Glucose, the key substrate in the microbiological changes occurring in meat and certain meat products. Biotechnology and Applied Biochemistry, 10, 203–231.

    CAS  Google Scholar 

  • Nychas, G. J. E., & Tassou, C. C. (1997). Spoilage process and proteolysis in chicken as detected by HPLC. Journal of the Science of Food and Agriculture, 74, 199–208.

    Article  CAS  Google Scholar 

  • Osborne, B. G., Fearn, T., & Hindle, P. H. (1993). Practical NIR spectroscopy with applications in food and beverage analysis. Harlow: Longman Scientific & Technical.

    Google Scholar 

  • Pittard, B. T., Freeman, L. R., Later, D. W., & Lee, M. L. (1982). Identification of volatile organic compounds produced by fluorescent pseudomonads on chicken breast muscle. Applied and Environmental Microbiology, 43(6), 1504–1506.

    CAS  Google Scholar 

  • Poole, L., & Finney, J. L. (1984). Sequential hydration of dry proteins: a direct difference IR investigation of sequence homologs lysozyme and α-lactalbumin. Biopolymers, 23, 1647–1666.

    Article  CAS  Google Scholar 

  • Rodriguez-Saona, L. E., Khambaty, F. M., Fry, F. S., & Calvey, E. M. (2001). Rapid detection and identification of bacterial strains by Fourier transform near-infrared spectroscopy. Journal of Agricultural and Food Chemistry, 49, 574–579.

    Article  CAS  Google Scholar 

  • Rodriguez-Saona, L. E., Khambaty, F. M., Fry, S. F., Dubois, J., & Calvey, E. M. (2004). Detection and identification of bacteria in a juice matrix with Fourier transform-near infrared spectroscopy and multivariate analysis. Journal of Food Protection, 67(11), 2555–2559.

    CAS  Google Scholar 

  • Rutledge, D. N., Barros, A. S., & Gaudard, F. (1997). ANOVA and factor analysis applied to time domain NMR signals. Magnetic Resonance in Chemistry, 35, S13.

    Article  CAS  Google Scholar 

  • Rutledge, D. N., Barros, A. S., & Giangiacomo, R. (2001). Interpreting near infrared spectra of solutions by outer product analysis with time domain NMR. In G. A. Webb, P. S. Belton, A. M. Gil & I. Delgadillo (Eds.), Magnetic resonance in food science: a view to the future (pp. 179–192). Cambridge, UK: Royal Society of Chemistry.

    Chapter  Google Scholar 

  • Rutledge, D. N., & McIntyre, P. A. (1992). Proposed European implementation of the JCAMP-DX data file transfer format. Chemometrics and Intelligent Laboratory Systems, 16, 95–101.

    Article  CAS  Google Scholar 

  • Sams, A. R. (2001). Poultry meat processing, Inc NetLibrary (pp. 165–170). Ohio: CRC Press.

    Google Scholar 

  • Shao, Y., & He, Y. (2009). Measurement of soluble solids content and pH of yogurt using visible/near infrared spectroscopy and chemometrics. Food and Bioprocess Technology, 2(2), 229–233.

    Article  CAS  Google Scholar 

  • Sinija, V. R., & Mishra, H. N. (2009). FTNIR spectroscopic method for determination of moisture content in green tea granules. Food and Bioprocess Technology. doi:10.1007/s11947-008-0149-8.

  • Suthiluk, P., Saranwong, S., Kawano, S., Numthuan, S., & Satake, T. (2008). Possibility of using near infrared spectroscopy for evaluation of bacterial contamination in shredded cabbage. International Journal of Food Science & Technology, 43, 160–165.

    Article  CAS  Google Scholar 

  • Watson, C. A. (1977). Near infrared reflectance spectrophotometric analysis of agricultural products. Analytical Chemistry, 49, 835A–840A.

    Article  CAS  Google Scholar 

  • Williams, P., & Norris, K. (eds). (2001). Near-infrared technology in the agricultural and food industries (2nd ed.). St. Paul, MN: American Association of Cereal Chemists.

    Google Scholar 

  • Woodcock, T., Fagan, C. C., O’Donnell, C. P., & Downey, G. (2008). Application of near and mid-infrared spectroscopy to determine cheese quality and authenticity. Food and Bioprocess Technology, 1(2), 117–129.

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank Prof. Douglas N. Rutledge for the use of the CATS software that allowed OPA analysis.

Author information

Authors and Affiliations

  1. Teagasc, Ashtown Food Research Centre, Ashtown, Dublin, 15, Ireland

    Dimitris Alexandrakis & Gerard Downey

  2. College of Life Sciences, School of Agriculture, Food Science and Veterinary Medicine, Agriculture and Food Science Centre, University College Dublin, Belfield, Dublin, 4, Ireland

    Dimitris Alexandrakis & Amalia G. M. Scannell

Authors
  1. Dimitris Alexandrakis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gerard Downey
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Amalia G. M. Scannell
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dimitris Alexandrakis.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Alexandrakis, D., Downey, G. & Scannell, A.G.M. Rapid Non-destructive Detection of Spoilage of Intact Chicken Breast Muscle Using Near-infrared and Fourier Transform Mid-infrared Spectroscopy and Multivariate Statistics. Food Bioprocess Technol 5, 338–347 (2012). https://doi.org/10.1007/s11947-009-0298-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11947-009-0298-4

Keywords

Search

Navigation